Journal ArticleArtificial Intelligence in the Life Sciences · June 1, 2024
Active machine learning is an established and increasingly popular experimental design technique where the machine learning model can request additional data to improve the model's predictive performance. It is generally assumed that this data is optimal f ...
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Journal ArticleNature nanotechnology · June 2024
Owing to their distinct physical and chemical properties, inorganic nanoparticles (NPs) have shown promising results in preclinical cancer therapy, but designing and engineering them for effective therapeutic purposes remains a challenge. Although a compre ...
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Journal ArticleNat Rev Drug Discov · May 2024
Prodrugs are derivatives with superior properties compared with the parent active pharmaceutical ingredient (API), which undergo biotransformation after administration to generate the API in situ. Although sharing this general characteristic, prodrugs enco ...
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Journal ArticleJournal of pharmaceutical sciences · March 2024
Triggerable coatings, such as pH-responsive polymethacrylate copolymers, can be used to protect the active pharmaceutical ingredients contained within oral solid dosage forms from the acidic gastric environment and to facilitate drug delivery directly to t ...
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Journal ArticleNature biomedical engineering · March 2024
In vitro systems that accurately model in vivo conditions in the gastrointestinal tract may aid the development of oral drugs with greater bioavailability. Here we show that the interaction profiles between drugs and intestinal drug transporters can be obt ...
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Journal ArticleNature computational science · February 2024
Computation promises to accelerate, de-risk and optimize drug research and development. An increasing number of companies have entered this space, specializing in the design of new algorithms, computing on proprietary data, and/or development of hardware t ...
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Journal ArticleBeilstein journal of organic chemistry · January 2024
Active learning allows algorithms to steer iterative experimentation to accelerate and de-risk molecular optimizations, but actively trained models might still exhibit poor performance during early project stages where the training data is limited and mode ...
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Journal ArticleNature reviews. Drug discovery · November 2023
Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to e ...
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Journal ArticleJournal of cheminformatics · October 2023
Established molecular machine learning models process individual molecules as inputs to predict their biological, chemical, or physical properties. However, such algorithms require large datasets and have not been optimized to predict property differences ...
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Journal ArticleDigital Discovery · August 1, 2023
Data subsampling is an established machine learning pre-processing technique to reduce bias in datasets. However, subsampling can lead to the removal of crucial information from the data and thereby decrease performance. Multiple different subsampling stra ...
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Journal ArticleJournal of chemical information and modeling · August 2023
Marginalized graph kernels have shown competitive performance in molecular machine learning tasks but currently lack measures of interpretability, which are important to improve trust in the models, detect biases, and inform molecular optimization campaign ...
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Journal ArticleMatter · March 2, 2022
Nucleic acids are enabling a new generation of therapeutics and vaccines to treat and prevent a range of diseases. While these therapies have typically been limited to parenteral dosing, patients and clinicians prefer oral dosage forms. Furthermore, oral d ...
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Journal ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · December 2021
Continuous monitoring in the intensive care setting has transformed the capacity to rapidly respond with interventions for patients in extremis. Noninvasive monitoring has generally been limited to transdermal or intravascular systems coupled to transducer ...
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Journal ArticleAnnals of intensive care · December 2021
BackgroundThe concomitant occurrence of the symptoms intravascular hypovolemia, peripheral edema and hemodynamic instability is typically named Capillary Leak Syndrome (CLS) and often occurs in surgical critical ill patients. However, neither a un ...
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Journal ArticleCell Reports Physical Science · September 22, 2021
Biological screens are plagued by false-positive hits resulting from aggregation. Methods to triage small colloidally aggregating molecules (SCAMs) are in high demand. Herein, we disclose a neural network to flag such entities. Our data demonstrate the uti ...
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Journal ArticleNature nanotechnology · June 2021
Nanoformulations of therapeutic drugs are transforming our ability to effectively deliver and treat a myriad of conditions. Often, however, they are complex to produce and exhibit low drug loading, except for nanoparticles formed via co-assembly of drugs a ...
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Chapter · January 1, 2021
Active machine learning is an experimental design approach that puts machine learning models in the driver seat of data acquisition and automated optimization. Introduced to drug discovery approximately 15 years ago, a handful of impressive studies have re ...
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Journal ArticleCell Reports Physical Science · November 18, 2020
Optimizing reaction conditions depends on expert chemistry knowledge and laborious exploration of reaction parameters. To automate this task and augment chemical intuition, we here report a computational tool to navigate search spaces. Our approach (LabMat ...
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Journal ArticlePharmaceutical research · October 2020
PurposeA multitude of different versions of the same medication with different inactive ingredients are currently available. It has not been quantified how this has evolved historically. Furthermore, it is unknown whether healthcare professionals ...
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Journal ArticleNature biomedical engineering · May 2020
Monolayers of cancer-derived cell lines are widely used in the modelling of the gastrointestinal (GI) absorption of drugs and in oral drug development. However, they do not generally predict drug absorption in vivo. Here, we report a robotically handled sy ...
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Journal ArticleCell reports · March 2020
Inactive ingredients and generally recognized as safe compounds are regarded by the US Food and Drug Administration (FDA) as benign for human consumption within specified dose ranges, but a growing body of research has revealed that many inactive ingredien ...
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Journal ArticleDrug discovery today. Technologies · December 2019
Active machine learning enables the automated selection of the most valuable next experiments to improve predictive modelling and hasten active retrieval in drug discovery. Although a long established theoretical concept and introduced to drug discovery ap ...
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Journal ArticleScientific reports · May 2019
Identifying potential protein-ligand interactions is central to the field of drug discovery as it facilitates the identification of potential novel drug leads, contributes to advancement from hits to leads, predicts potential off-target explanations for si ...
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Journal ArticleNature chemistry · May 2019
Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay inte ...
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Journal ArticleScience translational medicine · March 2019
Oral forms of medications contain "inactive" ingredients to enhance their physical properties. Using data analytics, we characterized the abundance and complexity of inactive ingredients in approved medications. A majority of medications contain ingredient ...
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Journal Article · 2019
Nanoformulations are transforming our capacity to effectively deliver and treat a myriad of conditions. However, many nanoformulation approaches still suffer from high production complexity and low drug loading. One potential solution relies on harnessing ...
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Chapter · January 2019
Fragment-like natural products play a pivotal role in natural product research given their improved synthetic and computational tractability as well as commercial availability compared to more complex natural product structures. A multitude of computationa ...
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Chapter · January 2018
High-throughput and high-content screening campaigns have resulted in the creation of large chemogenomic matrices. These matrices form the training data which is used to build ligand-target interaction models for pharmacological and chemical biology resear ...
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Journal ArticleFuture medicinal chemistry · March 2017
AimComputational chemogenomics models the compound-protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of prote ...
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Journal ArticleMolecular informatics · January 2017
Molecular descriptors capture diverse structural information of molecules and are a prerequisite for ligand-based similarity searching. In this study, we introduce topological matrix-based descriptors to virtual screening for hit discovery. We evaluated th ...
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Journal ArticleOncotarget · January 2017
Cancer stem cells (CSCs) play major roles in cancer initiation, metastasis, recurrence and therapeutic resistance. Targeting CSCs represents a promising strategy for cancer treatment. The purpose of this study was to identify selective inhibitors of breast ...
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Journal ArticleAngewandte Chemie (International ed. in English) · September 2016
The cyclodepsipeptide doliculide is a marine natural product with strong actin-polymerizing and anticancer activities. Evidence for doliculide acting as a potent and subtype-selective antagonist of prostanoid E receptor 3 (EP3) is presented. Computational ...
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Journal ArticleNature chemistry · June 2016
Natural products and their molecular frameworks have a long tradition as valuable starting points for medicinal chemistry and drug discovery. Recently, there has been a revitalization of interest in the inclusion of these chemotypes in compound collections ...
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Journal ArticleChemical science · June 2016
Active machine learning puts artificial intelligence in charge of a sequential, feedback-driven discovery process. We present the application of a multi-objective active learning scheme for identifying small molecules that inhibit the protein-protein inter ...
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Journal ArticleChemical communications (Cambridge, England) · January 2016
The promiscuous binding behavior of bioactive compounds forms a mechanistic basis for understanding polypharmacological drug action. We present the development and prospective application of a computational tool for identifying potential promiscuous drug-l ...
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Journal ArticleAngewandte Chemie (International ed. in English) · December 2015
Automated molecular de novo design led to the discovery of an innovative inhibitor of death-associated protein kinase 3 (DAPK3). An unprecedented crystal structure of the inactive DAPK3 homodimer shows the fragment-like hit bound to the ATP pocket. Target ...
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Journal ArticleAngewandte Chemie (International ed. in English) · September 2015
Fragment-like natural products were identified as ligand-efficient chemical matter for hit-to-lead development and chemical-probe discovery. Relying on a computational method using a topological pharmacophore descriptor and a drug database, several macromo ...
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Journal ArticleAngewandte Chemie (International ed. in English) · August 2015
Sustained identification of innovative chemical entities is key for the success of chemical biology and drug discovery. We report the fragment-based, computer-assisted de novo design of a small molecule inhibiting Helicobacter pylori HtrA protease. Molecul ...
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Journal ArticleDrug discovery today · April 2015
High-throughput compound screening is time and resource consuming, and considerable effort is invested into screening compound libraries, profiling, and selecting the most promising candidates for further testing. Active-learning methods assist the selecti ...
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Journal ArticlePlanta medica · April 2015
We present the application of the generative topographic map algorithm to visualize the chemical space populated by natural products and synthetic drugs. Generative topographic maps may be used for nonlinear dimensionality reduction and probabilistic model ...
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Journal ArticleAngewandte Chemie (International ed. in English) · January 2015
We report a multi-objective de novo design study driven by synthetic tractability and aimed at the prioritization of computer-generated 5-HT2B receptor ligands with accurately predicted target-binding affinities. Relying on quantitative bioactivity models ...
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Journal ArticleNature chemistry · December 2014
Natural products have long been a source of useful biological activity for the development of new drugs. Their macromolecular targets are, however, largely unknown, which hampers rational drug design and optimization. Here we present the development and ex ...
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Journal ArticleChimia · September 2014
Predicting the macromolecular targets of drug-like molecules has become everyday practice in medicinal chemistry. We present an overview of our recent research activities in the area of polypharmacology-guided drug design. A focus is put on the self-organi ...
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Journal ArticleAngewandte Chemie (International ed. in English) · July 2014
The discovery of pyrrolopyrazines as potent antimalarial agents is presented, with the most effective compounds exhibiting EC50 values in the low nanomolar range against asexual blood stages of Plasmodium falciparum in human red blood cells, and Plasmodium ...
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Journal ArticleProceedings of the National Academy of Sciences of the United States of America · March 2014
De novo molecular design and in silico prediction of polypharmacological profiles are emerging research topics that will profoundly affect the future of drug discovery and chemical biology. The goal is to identify the macromolecular targets of new chemical ...
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Journal ArticleTrends in molecular medicine · December 2013
Epigenetic effects are exerted by a variety of factors and evidence increases that common drugs such as opioids, cannabinoids, valproic acid, or cytostatics may induce alterations in DNA methylation patterns or histone conformations. These effects occur vi ...
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Journal ArticleChemical Science · March 1, 2013
Drug discovery programs urgently seek new chemical entities that meet the needs of a demanding pharmaceutical industry. Consequently, de novo ligand design is currently re-emerging as a novelty-generating approach. Using ligand-based de novo design softwar ...
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Journal ArticleJournal of proteome research · September 2012
Selected reaction monitoring mass spectrometry is an emerging targeted proteomics technology that allows for the investigation of complex protein samples with high sensitivity and efficiency. It requires extensive knowledge about the sample for the many pa ...
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Journal ArticleComputational biology and chemistry · December 2010
Understanding evolution at the sequence level is one of the major research visions of bioinformatics. To this end, several abstract models--such as Hidden Markov Models--and several quantitative measures--such as the mutual information--have been introduce ...
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